A COMPUTER SYSTEM AND METHOD FOR MEASURING AN ANALYTE CONCENTRATION WITH AN AFFINITY ASSAY

Abstract: A method for improved measurement of a concentration of an analyte in a sample having an unknown concentration of the analyte includes using an affinity assay having an experimental indicator. Standard and control samples, each having a known concentration of the analyte, are assayed and a response, an amount of experimental indicator emitted for each sample, is detected. The unknown sample is also assayed and its response is detected. A prior probability density is supplied for the unknown concentration, along with a model for a standard curve relating an expected response to a concentration of the analyte. A posterior density, having a median, for the unknown concentration is generated based on the supplied prior density, the supplied model, and the responses for the standard samples, the control samples and the unknown samples, by applying Bayes' rule. The median of the posterior density may be provided as the measure of concentration of analyte in the unknown sample, with an accuracy defined by the standard deviation of the posterior density. The standard curve preferably accounts for relative error due to experimental variation by using a random variable to represent variations on the analyte concentration scale due to variability in the preparation of samples in the laboratory. The minimal detectable dose for a single assay run may also be determined.

Description: A COMPUTER SYSTEM AND METHOD FOR MEASURING AN ANALYTE CONCENTRATION WITH AN AFFINITY ASSAY Field of the Invention The invention is related to computer systems and methods used with an affinity assay to measure an analyte concentration and to determine the accuracy of such a measurement. Background of the Invention Affinity assays are important measurement tools used in clinical and research laboratories for determining the concentration of an analyte in a sample. The term "analyte" refers broadly to any substance (a ligand) capable of being detected by formation of a complex with a corresponding anti-ligand. Such complex formation is an integral part of various classes of affinity assays, including for example, immunoassays (such as radioimmunoassays and immunoradiometric assays) and protein-binding assays (such as radioreceptor assays). Thus, the analyte may be an antigen or antibody in an immunoassay, or a protein or its cognate in a protein-binding assay. Such assays may take a variety of formats. In a direct assay format, a complex is formed between the analyte and the anti-ligand. In an indirect assay format such as a competitive assay, a complex is formed between a competitor and the anti-ligand. The competitor competes with the analyte for specific attachment to the anti-ligand. To detect the presence of an analyte using an affinity assay, one of the members of a complex is labelled with a tag which is capable of being detected. Examples of classes of such tags include radioisotopes, e.g. 125-[I]; enzymes, e.g., horse radish peroxidase; fluorescent compounds; bioluminescent compounds, e.g., luciferin; and chemiluminescent compounds, e.g., acridinium esters. Each tag emits a corresponding experimental indicator, such as gamma radiation, light, fluorescent light or enzyme activity, which may be detected. The response, i.e., the amount of experimental indicator detected, for a given concentration of analyte in a sample varies probabilistically. This variation is known as response error, and is Poisson for gamma radiation and is either Normal or log-Normal for the other types of experimental indicators noted above. The amount of variation may change with the mean level of the response. For a given concentration of analyte in a sample the response may also vary due to variation in the performance of the assay from one assay run to the next or due to variation in the preparation of the assayed samples. This variation may be due to errors in pipetting or mixing, incomplete separation between bound and unbound sample components, variation in reagent quality, or variation in response measurement time, incubation, temperature or centrifuging conditions. This variation introduces a random error in the concentration of the analyte. The relative error due to such experimental variation is experimental error. Because of both response error and experimental error, measuring the concentration of an analyte in a sample using an affinity assay involves an application of formal statistical analysis based on the responses from data from a number of samples, which ensures accurate data interpretation and maintains quality control. Statistical analysis is also used to determine the accuracy of an inferred concentration. Conventionally, using an affinity assay to measure an unknown concentration of analyte in a sample involves analyzing three sets of samples: standard samples, control samples, and the unknown analyte sample. Standard samples are prepared by the assay kit manufacturer in accordance with World Health Organizations (WHO) or the United States National Bureau of Standard (USNBS) specifications and contain known concentrations of the analyte, spanning the range of concentrations which are sufficient to establish a standard curve from which the concentration of the unknown sample can be inferred. Preferably, this range includes the concentrations which are believed to be important. The control samples contain known concentrations of the analyte and are used to chart assay stability and experimental error. Typically, the controls are prepared from samples assayed previously in the current laboratory. Typically, a large number of unknown samples are assayed together with the standards and controls in what is commonly called a single assay run. In a single assay run, standard samples are assayed first in replicate to obtain a measurement of an amount of the experimental indicator (i.e., a response) by each replicate. For example, with a radioligand binding assay the number of radioactive counts emitted in a given time period by each sample and its replicate is recorded. A standard curve, which relates a known concentration of analyte to an expected response, is estimated, in a manner to be described in more detail below, based on only the known concentrations and corresponding responses for the replicates of the standard samples. The samples containing unknown concentrations of analyte are then assayed, typically in replicate, along with replicates of the control samples interspersed between the unknown samples. The perception that better quality control can be achieved by automated assay procedures, the rising cost of assaying large numbers of samples and the desire to reduce radioactive waste (resulting from assays using radioisotopes) production have led many laboratories to assay unknown samples as singlets, not replicates. The response for each unknown and control sample replicate is recorded. The analyte concentration in each unknown sample is inferred by finding the ordinate on the estimated standard curve which has the response for the unknown sample as its abscissa. The standard curve is conventionally estimated by fitting a curve to the known concentrations for the replicates of only the standard samples and their associated responses using a non-linear, weighted least squares technique. The curve has empirically been found to correspond to a four-parameter logistic model (4-PL). This curve sometimes approximates the curve described by the mass-action law for the underlying chemical reaction. A conventional model used for estimating the standard curve may be described in the following manner: Let H denote a concentration of analyte and let h = logH. The log scale is commonly used to describe many analyte concentrations since the biologically possible range of concentrations often covers several orders of magnitude. Let Y be the response recorded from assaying sample. For notational purposes, the control, the standard and the unknown samples are denoted respectively as groups l, 2 and 3. Let N. be the number of samples in group i,H. = 1 m 1 [H. ... ,H. ._. ] be the analyte concentrations in the itn group samples, h. = [h. .,...,h. „.]T be the log analyte concentrations of the i group samples and let Y i;l,l i;N i ,ι Yi = (1) ' i;l,q , i;N i ,q be respectively the N. x q matrix of measurements obtained from measuring the amount of experimental indicator emitted by the samples assayed in group i, for i = 1,2,3, where q is the number of replicates (usually 1 or 2). In an immunoassay or protein-binding assay, the expected measure of experimental indicator is usually a monotonic function of the analyte concentration, to which a four parameter logistic (4-PL) model has been found empirically to fit well. A parametrization of the 4-PL model is: max- m E(Y | h , θ) = + min = g( h , θ) ( 2 ) 1 + exp ( β-γh) where θ = [max, γ, β, min] T. Other parametπzations are also well-known. If ε is set to be -γ, with p = exp(β/γ), and θ* = [max, ε, p,min] T then (2) may be rewritten as max-mm g(H,θ*) = + min (3) 1 + (H)ε P which is the conventional "Rodbard" model. See either "Statistical Analysis of Radioligand Assay Data," Methods in Enzymology, Volume 37, 1975, pp. 3-22, by D. Rodbard and G.R. Frazier ("Rodbard") or "Radioligand and Assay,", Biometrics, Volume 32, 1976, pp. 721-740, by D.J. Finney ("Finney 1976") for a description of this model. These references and all others cited in this document, are expressly incorporated by reference. A graph representing equation (2) is shown in Fig. 1, where the ordinate represents the concentration H and the abscissa represents the expected measure E(Y) . The 4-PL model is useful because it seems to fit empirically the mass-action law equations governing the kinetics of some affinity assays under some conditions which have been described in the art. See, for such a description, "Interrelations of the various mathematical approaches to radioimmunoassay, " Clinical Chemistry, Volume 29, 1983, pp. 284-289, by A.A. Fernandez et al . ("Fernandez"), or "Response curves for Radioimmunoassay," Clinical Chemistry, Vol. 29, 1983, pp. 1762-1766, by D.J. Finney ("Finney 1983") or "A Comparison of Logistic and Mass-Action Curves for Radioimmunoassay Dara, ' in Clinical Chemistry, Vol. 29, 1983, pp. 1757-1761 ("Raab") . After a standard curve is estimated using the measured responses for the standard samples, a concentration of analyte is estimated for each of the unknown samples using the estimated standard curve. The average concentration of replicates, computed from the individual estimates, is reported as the estimated concentration in the unknown sample, provided that the individual estimates do not differ appreciably from each other. The average and the standard deviation of the individual estimated concentrations for the replicates of an unknown sample are used to compute the intra-assay coefficient of variation for the estimated analyte concentration. That is, they are used to quantify a measure of accuracy of the estimate. The estimates of the concentrations for the control samples are used to compute the intra-assay coefficients of variation at selected concentrations throughout the range of the assay. The inter-assay coefficients of variation are computed from the estimated concentrations for the control samples obtained from different assay runs. Nearly every laboratory in the world which uses affinity assays uses some form of the method described above. However, several theoretical and practical problems with this method exist. First, for most assays the experimental error is neither routinely nor formally assessed on the analyte concentration scale. One current approach to accounting for variations in the response beyond that due to expected response error from known physical properties of the tag is to model all variation in the response data as a simple polynomial function of the expected response for a given analyte concentration, i.e. as response error. If the polynomial order is one and there is no constant term, the data contains only response error, whereas if it greater than one, there is extra response variation (either Poisson or Gaussian) as well. For many radioligand binding assays the best choice of exponent has been found to lie between one and two (See Finney 1976) . Rodbard also suggests the "relative error model" as a method for studying the effect of experimental variation on the extra-Poisson variation in the response data of a radioligand binding assay. See also "Statistical aspect of radioimmunoassay," by D. Rodbard, in Principles of Competitive Protein Binding Assays, Ed. W.D. Odell et al . , Philadelphia: Lippincott, 1971, pp. 204-259, ("Rodbard 1971"), for a similar suggestion. With this method, any experimental variation is represented by an estimate of the amount of extra-Poisson variation in the observed measure of radioactivity of the standards as determined by the type of weights used in fitting the standard curve with nonlinear regression methods. Thus, experimental variation is modelled in terms of the response instead of explicitly as error in the concentration of the samples being assayed. It is important to estimate experimental variation because it affects the measurement accuracy of all unknown samples, and gives information to laboratory personnel concerning the consistency and quality of materials and technique. Because of the manner in which experimental variations are considered by models used in these methods, experimental error cannot be accurately determined on the analyte scale. A second problem with conventional methods is that it is not possible to obtain a reasonable determination of the accuracy of the estimated analyte concentration for a singlet. Therefore replicate unknown samples must be assayed. Third, because both the response and the concentration of analyte are random variables, directly inverting the estimated standard curve describing the expected response for a given analyte concentration does not describe correctly the expected analyte concentration for a given response. See Non-linear Regression, by G.A.F. Seber and C.J. Wild, (New York: John Wiley & Sons), 1989, pp. 247-250 ("Seber"). Although any resulting inaccuracy in concentration estimates is minimal, the effect of this inaccuracy on the determination of the accuracy of these estimates is substantial, as described below. Fourth, because the minimal detectable dose (MDD) is not determined as part of a single assay run, a predetermined MDD does not correctly represent the smallest quantity of analyte detectable by a given assay run. The assay MDD or sensitivity (defined as the smallest analyte concentration which the assay can just distinguish from an apparent blank concentration) is usually determined when the assay is first prepared by assaying a large number, e.g., 20 to 40, of replicates of samples with an apparent blank concentration (blank samples). With conventional assay procedures, the standard curve is estimated for a single assay run from ten to twelve observations, i.e., with five or six standards each assayed in duplicate, of which only one standard, and thus two observations, are from blank samples. Because the MDD is predetermined using a number of standards far in excess of the number used on a day-to-day basis in the laboratory, it cannot reliably be used as the MDD for a single assay run. Instead, the practice in many laboratories is to report only the analyte concentrations which exceed that of the smallest non-blank standard sample. The definition of the MDD provided by Rodbard 1978 is a conventional approximation to the upper limit of the 1 - α highest probability density (HPD) interval, where α is between 0 and 1, for the apparent blank analyte concentration and as such, considers the uncertainty in the determination of the apparent blank analyte concentration. However it does not consider the uncertainty in any other analyte concentrations. For further discussions of the MDD see "Statistical estimation of the minimal detectable concentration for radio immunoassays" by D. Rodbard in Analytical Chemistry, Vol. 90, 1978, pp. 1-12, ("Rodbard 1978"); "Determining the lowest limit of reliable assay measurement," by L. Oppenheimer et al . , in Analytical Chemistry, Vol. 55, 1983, pp. 638-643 ("Oppenheimer"); "Variance functions and minimal detectable concentration in assays," in Biometrika, Volume 75, Number 3, 1988, pp. 549-556, by M. Davidian et al . ("Davidian") . Finally, the validity of the estimation of accuracy obtained using conventional methods is questionable. While conventional methods allow for a systematic analysis of data, these methods rely on and are derived from large sample theory. The validity of inferences for affinity assays using large sample approximations is questionable since most standard curves are estimated from not more than a dozen observations, not from a large sample. This problem has been discussed in the art. See, for example, Davidian. Despite these problems, all laboratories currently use these conventional methods. An alternative method for obtaining an estimated concentration for an unknown sample, the application of Bayes' rule, has been suggested in "A Note on the Problem of Statistical Calibration" by T. Lwin and J.S. Maritz in Applied Statistics, Volume 29, pp. 135-141, 1980 ("Lwin"). To use Bayes' rule, a specification of a prior distribution for the analyte concentration in the unknown sample is required. However, the prior density specified by Lwin has been discredited as unrealistic for practical applications. (See Seber, p. 248). The prior density specified by Lwin is particularly unrealistic for use in measuring analyte concentrations using affinity assays. A Bayeεian approach to the estimation and use of a standard curve has been discussed generally in "A Bayesian Approach to Calibration" by I.R. Dunsmore, Journal of the Royal Statistical Society, Series B, Volume 31, pp. 396-405, 1968 ("Dunsmore"). However, in the standard curve described in Dunsmore, the expected response is assumed to be directly proportional to the quantity to be estimated. As such it is wholly inapplicable and too simplistic for affinity assays, for which a standard curve is typically described by a εigmoid fraction, such as the 4-PL model. Summary of the Invention A method for measuring a concentration of an analyte in a sample having an unknown concentration of the analyte in accordance with the invention involves using an affinity assay having an experimental indicator. Standard and control samples, each having a known concentration of the analyte are assayed and an amount of experimental indicator emitted by each sample is detected. The unknown sample is also assayed and its response is detected. A prior probability density is supplied for the unknown concentration, along with a model for a standard curve relating an expected response to a concentration of the analyte. A posterior denεity, having a median, for the unknown concentration is generated based on the supplied prior density, the supplied model, and the responses for the standard samples, the control samples and the unknown samples, by applying Bayes' rule. The median of the posterior density may be provided aε the measure of concentration of analyte in the unknown sample. In some instances, the mean or mode may be used as the estimate. The accuracy of an estimate is fully specified by the posterior density. A εu mary of this accuracy could be provided by the coefficient of variation, the εtandard deviation or other summaries of the dispersion of the posterior density. In one aspect of the invention, the posterior density is determined using estimated parameters for the model of the standard curve. These estimates may be determined using a weighted least-squares technique or by using maximum likelihood estimation. The parameters for the standard curve may be derived using only the εtandard samples, aε is done conventionally, or by using all of the sampleε, including unknown εampleε. The model of the εtandard curve may be baεed on the maεε-action law for the underlying chemical reaction or on a 4-PL model . The model iε preferably augmented to account for relative error due to experimental variation. Such an augmentation iε preferably done by using a random variable to represent variations on the analyte concentration scale due to variability in the preparation of samples in the laboratory. This random variable may be a Gaussian random variable, resulting in a probability density for the response for a given concentration which iε a Gaussian mixture of random variables. In the case of a radioligand binding assay, this probability density is a Gaussian mixture of Poisson random variables. In another aεpect of the invention, the parameterε of the standard curve are integrated out to account for all uncertainty in these parameters while deriving the inference for the concentration of an unknown sample. In another aspect of the invention, the minimal detectable doεe for a single asεay run is determined by determining the posterior density for apparently blank standard samples H . _ along with a 1-α highest probability density (HPD) interval where 0 < α < 1, such as a 95% HPD interval. The set E of random variables H which satisfy the condition that zero is the left endpoint of the 1-α HPD interval of the random variable H -H ι Q is determined. The minimal detectable dose of the assay run iε the supremum of the set of medians for the probability densities of the random variableε H in set E. One embodiment of the invention is a computer εyεtem for calibrating an assay, to enable an inference about an analyte concentration in a sample based on a measurement of an experimental indicator emitted from assayed sampleε, and to determine the accuracy of the inference, the minimal detectable concentration of an aεεay run, and to measure and separate the variation due to experimental materials and technique from responεe variation. The type of detector to be used is dependent on the type of experimental indicator for the asεay. For example, a luminometer iε uεed to detect chemiluminescence or bioluminescence. A spectrophorometer is typically used to detect fluorescence. A gamma detector is used to detect radioactivity. Several detectors are capable of detecting enzyme activity including colorimeters. The selection of an appropriate detector depends on how any enzyme product is to be measured. Brief Description of the Drawinqε In the drawings, Fig. l is a graph of an estimated standard curve according to a four parameter logistic model; Fig. 2 illustrates a typical arrangement of an affinity assay used with a computer to perform εtatistical analysis; Fig. 3 is a block diagram of a computer which may be used to analyze assay data; Fig. 4 is a flow chart describing how results of an asεay run may be analyzed; Fig. 5 iε a flow chart describing a second embodiment of how resultε of an aεεay run may be analyzed; Fig. 6 is a dataflow diagram of a computer system which performs analysis on assay data; Fig. 7 is a dataflow diagram of a second embodiment of a computer system which performs analysis on assay data; Fig. 8 is a module dependency diagram illustrating how different modules of the embodiment of the computer system of Fig. 6 are interrelated; Fig. 9 is a module dependency diagram illustrating how different modules of the embodiment of the computer system of Fig. 7 are interrelated; Fig. 10 is a graph representing a prior probability density for cortisol; Fig. 11 is a table of sample data of an assay for cortisol; Fig. 12 is a table of model parameter estimateε for the example of an aεεay for cortisol; Fig. 13 iε a graph representing the posterior density of the cortisol standards; Fig. 14 is a graph representing the posterior density of the cortisol controls; Fig. 15 is a table of the summary statisticε of the denεities shown in Figs. 13 and 14. Fig. 16 is a table of the εummary statisticε for the posterior or densitieε for the unknownε and the MDD; and Fig. 17 is a graph representing the relationship between the posterior densities of the zero cortisol concentration and of the minimal detectable dose of the example cortisol assay run. Detailed Description The preεent invention will be more completely underεtood through the following detailed deεcription which εhould be read in conjunction with the attached drawing in which similar reference numbers indicate similar structures. For the sake of simplicity this description is provided in the context of radioligand binding aεεays. However, the invention is not limited solely to such asεayε. Throughout this description, the following notation, and the notation used in the Background section, will be used: Let [x] denote the probability density of the random variable x, let P(x|λ) denote the probability of the Poisson random variable x with mean λ and let φ(x|μ,σ 2 ) denote specifically the density of a Gaussian random variable with mean μ and variance σ . Using an affinity assay to measure the concentration of an analyte in an unknown sample involveε well-known techniqueε uεed in many laboratorieε throughout the world, aε discussed above. For each sample, a responεe for a εa ple is meaεured by detecting with an appropriate detector the amount of experimental indicator emitted by the sample. Appropriate detectors are well-known devices which provide an output indicative of the meaεured resoonse. As shown in Fig. 2, a detector 40 has its output signal fed to a computer 42 via a cable 44. In a typical arrangement, the detector 40 is arranged so that it is enclosed with a number of samples, in test tubes 43, in a rotatable container 45. Each test tube 43 is placed under the detector 40 for a predetermined amount of time, typically sequentially, by periodically rotating the container 45. Many other arrangements for running the assay may be used, which are well-known in the art. The kind of detector 40 to be used is dependent on the type of experimental indicator for the assay. For example, a luminometer is used to detect chemiluminescence or bioluminescence. A spectrophotometer is typically used to detect fluorescence. A gamma detector is used to detect radioactivity. Several detectors are capable of detecting enzyme activity including colorimeters. The selection of an appropriate detector depends on how any enzyme product is to be measured. The connection of a detector 40 to a computer 42 can be done in any of many well-known ways so that the computer can store and process the signals from the detector 40. The computer 42 is used to perform statistical analysis on the responses obtained for the samples for each assay run in order to obtain meaningful results from the assay. Fig. 3 is a block diagram of a computer which is suitable for this purpose. Such a computer includes a central processing unit (CPU) 50 and a memory 52, typically a random access memory (RAM) , interconnected by a bus 5 . The computer also has input and output connections 56 and 58, also connected to the bus, which allow the computer 42 to communicate with output devices (not shown), such as a display or printer, and input devices, such as a keyboard, trackball, mouse, light pen, etc. Numerous other connections to the bus 54 may be provided to allow the computer to connect to other computers (not shown) via a network (not shown) . The computer may also include a nonvolatile storage 60, such as a hard disk, to RECTIFIED SHEET (RULE 91) enable storage of large amounts of data for extended periods of time. The computer is also connected to the detector 40 using an input connection 62. The applicants have implemented a system on an IBM-PC type computer, which has an Intel 80386 microprocesεor aε its CPU 50. The system has also been implemented on a workstation manufactured by SUN Microsystems of Cupertino, California. The system is preferably implemented using standard computer programming techniques to implement statiεtical analysis procedures as will be described in more detail below. For this purpose, the programming language FORTRAN was used. Standard programs may be used to control the input of data into the computer 42 from the detector 40. To asεist development of the computer program for statistical analysiε, a library of mathematical sub-routineε may be uεed to perform a number of standard mathematical functions. A suitable library of routines is the International Mathematical Standards Library (IMSL) Math/Library of FORTRAN Sub-routines for Mathematical Applicationε, available from IMSL of Houston, Texas. It should be understood that the specific computer languages and computer systems shown and described here do not limit the present invention. Many other computer systems, programming languages and programming techniques may be used to practice the present invention. The statistical analysis of assay data will now be described with reference to the flow chart illustrated in Fig. 4. The computer may be programmed to execute the steps of this method. In Fig. 4, the first step 70 of the method is to asεay the standards, controls and unknown samples. The standards and controls are assayed at least in replicate. The unknowns may be assayed either in singlet, in duplicate or in as many replicates as considered appropriate. To reduce costs and to reduce the amount of waste resulting from an aεεay run, for example radioactive wastes from radioligand binding assays, the unknowns are preferably assayed in singlet. Parameters for a εtandard curve for the assay are then estimated in step 72. This step involves the prescription of a suitable model of the expected responεe aε a function of concentration and other parameterε deεcribing the model of the function, along with the eεtimation of thoεe parameterε. Although this step can be performed using conventional modelε for the εtandard curve, and conventional estimation procedures, it is also posεible to uεe different models and estimation procedureε. Preεcription of a Model for the Standard Curve A reasonable prescription of a model for the expected responεe aε a function of concentration and other parameters defined by the physical properties of the assay iε typically provided by the masε-action law for the underlying chemical reaction of the assay. A difficulty with using the mass-action law has been that it does not always describe the empirical relation between the response and analyte concentration. (See Raab) However, it is expected that, aε the purity and specificity of reagentε improves, the mass-action law may be more appropriate to use aε a model of thiε expected response function. Otherwise, the standard 4-PL model is suitable. Other modelε having a εig oid form or part thereof are alεo εuitable. It iε preferable to εupplement the model for the expected response with a parameter which accounts for experimental variation. A suitable model is either the relative error model of Rodbard or the polynomial model of Finney 1976, both described above. However, it is preferable to εupplement the model of the expected response in εuch a way that the experimental error is explicitly modeled on the analyte concentration εcale aε a random variable. Aεεuming that each εample haε a relative error v=logV due to experimental variation and that v is a Gausεian random variable, the probability density of v is φ(v|0, σ ). The parameter σ 2 represents the experimental error variance and is assumed to be constant across all analyte concentrations for the standards, controls, and unknown samples. The significance of this repreεentation of relative error iε that it accountε for the fact that laboratory procedureε uεed to prepare εamples randomly perturb the concentrations by a relative amount V. Modeling experimental error aε a random variable on the analyte concentration scale has the effect of making the probability distribution of the response for a given concentration a continuous mixture of random variables. For a model of a radioligand binding assay, in which experimental error is modeled as a Gausεian random variable, the probability diεtribution of the reεponεe for a given concentration iε a Gauεεian mixture of Poisson random variables. Therefore, the distribution of the response depends on a concentration H, parameters θ which 2 describe the physical propertieε of the assay, and σ which describes the experimental error as a random variable. The formulation of the preferred model for the standard curve is thus aε followε. The probability denεity of the responεe measured from assaying using a radioligand binding assay the k replicate of sample j in group i given h.. = log(H..), [γ ijki h ij' v ij - θ] = p(γ i ki9< h ij + v ijk- θ >> < (4 > and the joint density J of Y.., and v... given h.., θ, and σ 2 ιjk ljk 3 l IS [γ i D <v ij i h ij' θ ' σ2]β t γ ijki h ij' v ijk' θ] φ< v ijki°' σ2 ' (5) where k = l,...q,j = l, ... ,N^ and i = 1,2,3. Integrating (5) with respect to v... yields the density of the response [Y ijk |h i;j , θ,σ 2 ] = πY ijk ,v ijk |h i;j , θ,σ 2 ]dv ijk (6) which is a Gaussian mixture of Poisson random variables. Estimation of model parameters The standard curve is typically derived using the known concentrations (for the standard samples) and their responses, along with a non-linear, weighted least squares technique, as described above. Although thiε method may be used, an alternative is to include the concentrations and responεeε for the controlε and the responses for the unknowns, and to estimate the parameterε of the model of the εtandard curve using a maximum-likelihood estimation technique. An alternative, as will be described in more detail below, is to integrate out the parameters θ and σ 2 to account for all uncertainty in θ and σ 2 while deriving the inference for the concentration of an unknown sample. The joint density of the response, where θ' represents 2 θ, andd σσ2 if uεed, and log analyte concentrationε in group i is Ni q [Yi,hi|θ'] = π π[Yi k |hi- j ,θ'][hi- j ] (7) j=ι k=ι for i = 1,2,3. The joint denεity of Y. , Y_ and Y_ , given θ and σ , is 3 Ni 2 [Y 1 ,Y 2 ,Y 3 |Θ' ] = π π _/ π [Yijklh_Lj,θ' ][hij]dhij (8) i=l j=l k=l where [h.. ] are given by equations (9) and (10) or (11) aε described below. The log-likelihood of θ and σ 2 is the 2 log of equation (8) viewed as a function of θ and σ . The maximum likelihood estimate of θ and σ 2 can be readily computed from the log-likelihood, by finding the values of θ and σ which maximize equation (8). Preliminary estimates for the models parameters may be obtained from the assay data, for example, by using the formulation described in Rodbard. Large values for the estimate of σ would warn the laboratory personal that the assay run was particularly affected by experimental error. The maximum likelihood eεtimate σ iε an estimate of the standard deviation of the log relative experimental error. The interpretation of this parameter is readily given by example. Suppose σ = 0.05, then 95% of the time the variation introduced into the measurement of the analyte by experimental error is leεε than + 10%. (= + 1.96* [exp(σ)-l]). The prior probability distribution [h] for the standards and controls, for the purpose of maximum likelihood estimation of θ and σ 2 , are as follows. The concentrations in these samples are known prior to asεay, therefore, appropriate prior densities for the analyte concentration in these samples are [h] ={ δ ' h = h ij and (9) 0, otherwise, for j = l,...,Ni and i = 1, 2, where δ is the Kronecker delta function. The prior denεity for unknowns is described in more detail below with equations (10) and (11). Application of Bayes' rule and prior denεity for unknownε Given the estimated maximum likelihood values for the parameters θ, and σ if used, Bayes' rule iε applied in step 74 (Fig. 4) to obtain a posterior probability distribution for the unknown concentration of a εample. Bayes' rule iε a well-known tool of εtatiεtical analyεiε, and iε described in many textbooks on probability and statistics, for example, Fundamentals of Applied Probability Theory, by Alvin W. Drake (New York: McGraw Hill Book Company, 1967), pages 250-257 ("Drake"). In order to use Bayes' rule, a well defined prior density for the unknown concentration must be specified. For any established assay system a laboratory has information on the range and frequency of values previously reported for the analyte. Despite this information, samples containing unknown concentrations of analyte are analyzed as if any value in the biologically possible range of concentrations were equally likely. (See "Sensitivity, specificity, and cost-effectiveneεε of thyrotropin aεεay in the diagnosis of thyroid disease in ambulatory patients," by E.T. De los Santos et al. , in Archives of Internal Medicine, Volume 149, 1989, pp. 526-532, ("De los Santos"), or "Monitoring therapy in patients taking levothyroxine, " by M. HeIfand et al., in Annals of Internal Medicine, Volume 113, 1990, pp. 450-454 ("Helfand"), or "Accelerated bone loss in hypothyroid patients overtreated with L-thyroxine, " by G.M. Stall et al. , in Annals of Internal Medicine, Volume 113, 1990, pp. 265-269 ("Stall").) With such an analysiε, a given asεay experiment can provide information independent from that already amassed from prior asεayε, the diεease εtate of a patient or the efficacy of a therapeutic intervention. This analysis suggests that the prior probability denεity for a sample with an unknown concentration of analyte is uniform on most of its support. The support of this denεity iε defined largely by the working range of the assay, i.e., the range acrosε which the aεεay can reliably detect any analyte concentration. Thiε range iε eεtablished when the asεay is developed using information on the biological range of concentrations of the analyte coupled with theoretical guidelines and empiricism to decide how much of each chemical species, e.g. antibody, radiolabelled antigen, and buffer etc., should comprise the aεεay system. The working range of the asεay is usually specified as the intervals of concentrationε between the εmalleεt and largest non-blank εtandard. To permit the MDD to be eεtimated for any single assay run which includes the apparently blank standards, the interval over which the prior denεity haε uniform εupport must extend to a concentration below the smallest non-blank εtandard. Given these considerations, the form of a prior probability density for any analyte concentration may be described by the following function: κ(l + t 2 /v) "(v+1) 2 h < h, = (h-h α )/s [h] = K , h ftJ h <_ h h , (10) κ(l + t 2 /v) "( +1)/2 , h > h h , t = (h-h h )/s where v is the number of degrees of freedom for a t-denεity, h. iε the log concentration of analyte resulting from a single molecule of the analyte being placed in the unit volume of the asεay, h, iε the log of twice the concentration of the asεay' s higheεt standard, = .85/(h h - h α ), s = .15(h h - h )/(.85vl/2B(l/2, v/2), and B(l/2, v/2) iε the beta function with parameterε 1/2 and v/2. The prior denεity haε 85% of itε mass distributed uniformly between h„ and h, , and has the tails of a t-density on v degrees of freedom on either side of these two log concentrations where the remaining 15% of the masε iε evenly divided. The log concentration h Q is the log of the theoretical lower limit of the senεitivity of the assay since this is the smallest concentration that could be detected if there were no error in the asεay system. The log concentration h, is more difficult to specify on theoretical grounds. For practical purposeε it haε been placed at the log of twice the concentration of the higheεt εtandard of the aεεay. A εuitable prior density may practically vary from the form described by equation (10). A εuitable prior density can be deεcribed by a continuous function with a large variance, i.e., the standard deviation of the density repreεented on the concentration εcale is greater than about one-quarter of the range of the support of the density, and haε a support large enough to account for high concentrations and to allow computation of the MDD. Small changes in such a prior density should result in negligible differences in the inferences for the unknown concentrations. As an example of an alternative prior density, a uniform distribution may be defined on the concentration (H) scale such that [H] = H ~ o for 0 —< H —< Ho, where Ho = exp(h,h); and zero otherwiεe. When tranεformed to the log-concentration scale, such a prior denεity would take the form ΘXp(h) , for h < h, ( 1 1 ) [ h ] = { , eχ p< ( 0 , otherwise, Posterior denεity of the analyte concentration The conditional density of the log analyte concentration 2 given maximum likelihood estimateε of θ and σ if used, below represented as θ' , and the j set of reεponses Y ij = CY ijq Y ijq ] in the 9rou i is [hi.].| 1 Yi.].,θ~] = [Yl.]. l 1 hl.]. ,θ"][hI.D.]/[Yι.j. |θ~] (12) ' where and, r vijlθ-i = ; [Yijl . -.e-ith. -idh.. (i 4 > for i = 1,2,3 and j = 1,..., N. and q is the number of replicates. The log concentration scale is uεed becauεe it allows a convenient description of a broad range of concentrations for an analyte. The posterior density of the analyte concentration H^. given the responεeε Y.. and the parameters θ " iε derived by applying in equation (12) the transformation of variables H.. = exp(h..) to obtain the posterior probability density [ H ϋ i γ i- , θ- ] (15) for i = 1,2,3 and j = l,...,N i Given θ~ and Y.. for sample j in group i, equation (15) describes a posterior density from which inferences can be made about the analyte concentration associated to a given response. It is worth noting that the posterior probability density for both the standards and the controls has two different forms. The first waε described above in connection with the description of the maximum-likelihood eεtimates Θ A . However, once θ " is estimated, the accuracy of the assay in measuring specified standard and control analyte concentrations can be evaluated by computing the posterior denεitieε for these two groups uεing the prior probability denεity for the unknownε defined in either of equationε (10) or (11) inεtead of the prior denεity in equation (9). Given a posterior density [H|Y,Θ~] for an unknown analyte concentration H , the concentration of analyte in the respective unknown samples may be inferred or eεtimated in εtep 76 (Fig. 4). The accuracy of that inference or estimate can also be determined. In particular, given the posterior probability density [H|Y..,θ~] for i = 1,2,3 and j = 1, ... ,N, , let μ• • , σ .. and m. • denote respectively its posterior mean, posterior variance and posterior median. The analyte concentration in the j" sample of group i is inferred to be . (because .. is ^J ^D independent of the tranεfor ation between h and H) . The mean or mode may alεo be provided as the inferred analyte concentration. The accuracy of the estimate of the concentration, e.g., m.., is fully specified by the posterior density. A summary of this accuracy could be given by the coefficient of variation (which is σ../μ..), the εtandard deviation 1 D Cj or other summaries of the disperεion of the poεterior denεity. Alternative Derivation of Poεterior Densities As described above, it is poεεible to integrate out the possible parameters of the εtandard curve, rather than to provide an estimate of them. This method accounts for uncertainty in the estimation of θ and σ 2. For this aspect of the invention, the poεterior denεity of the parameterε θ and σ iε given by the following equation (16) where θ' representε θ, a 2 : [θ' |Y 1 ,Y 2 ,Y 3 ,h 1 ,h 2 ] o π([Y 1 ,Y 2 |h 1 ,h 2 ,θ' ])jπ([Y 3 |h 3 ,θ' ])[h 3 ]dh 3 [θ' |h 1 ,h 2 ] (16) where [θ'|h _] iε the prior diεtribution of θ' for a given aεεay in which the concentrations of the standards are specified, and π iε the product over, reεpectively, all the assay sa pleε within the standards and controls and all the asεay sampleε within the unknowns. To specify the prior distribution of θ', a large number of point estimateε of θ' are taken from a corresponding large number of aεεay runs. To facilitate the uεe of this information in the determination of [θ'3, a multivariate Gausεian distribution is used to approximate this prior density. A multidimensional histogram which representε a prior probability density for θ' is formed using these estimates. A multivariate mean and a variance/cova iance matrix of the estimates of θ ' are determined. The multivariate mean and variance/ccva iance matrix of the multivariate Gausεian are taken to be the determined multivariate mean and variance/covariance matrix. Suitable transformation of each model parameter may be necessary to improve the degree to which the multivariate Gaussian is a good approximation for the prior probability density of θ' . For example, in modelε including σ εuch tranεformation iε typically appropriate. Gibb'ε εampling or the marginal-Pearεon algorithms give a large number N of draws of θ' from the posterior of equation (16). These N drawε need only be taken once, and can be uεed in the calculation of poεterior denεitieε for all unknown εampleε in a εingle aεsay run. These algorithmε are deεcribed in, respectively, "Bayesion Computation via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods", by A.F.M. Smith and G.O. Robertε, in J.R.Statiεt■Soc■ , Vol. 55, No. 1, 1993, pp. 3-23, and "Laplacian and Uniform Expanεionε with Applicationε to Multidimenεional Sampling", by S.J. Skates, PhD. Disεertation, Univerεity of Chicago, Department of Statistics, August 1987. For each draw θ , [Y 3 |h 3 ,θ Hh.,] is calculated as a function of h_ and is normalized to obtain a posterior density. The inferential (posterior) density for h 3j iε: [ 3 . |Y 1 ,Y 2 ,h 1 ,h 2 ,Y 3 .]= X[h 3j |Y 3j ,θ' ][θ' |Y 1 ,Y 2 ,h 1 ,h 2 ,Y 3 ]dθ' (17) where [h_ . | _ . ,θ' ] iε the predictive denεity of h_. for a given θ*, and [θ' |Y, ,Y 2 , h ± ,h 2 ,Y 3 ] is the posterior distribution of θ' given the asεay data. To calculate the predictive denεity for given θ* we have [h 3 ή|Yι,Y2,h 1 ,h 2 ,Y 3 ,θ'] = [Y 3 j|h 3 j,θ*][h 3 ] (18) -H γ 3j |h 3 j,θ][h 3 ]dθ The integral in (18) is approximated by an average cf the predictive denεitieε [h_ . |Y_ . ,θ*] [h_] . Finally, to expreεs this poεterior denεity on the concentration scale we uεe the exponential transformation analagous to equation 15. The average value of the σ component of the N θ* ' s may be used as a point estimate of the standard deviation of the log relative experimental error. The average density of all N densities is the final density of log analyte concentration for an unknown sample given the assay result data. In this aspect of the invention these stepε (indicated at 80 in Fig. 5) replace steps 72 and 74 of Fig. 4. Minimal detectable dose for an assay run The minimal detectable dose for an assay run is determined in step 78, in accordance with the following definition. Given 0 < α < 1, and a posterior density for the analyte concentration H , Q corresponding to the assay of the apparent blank concentration standards, let E denote the set of random variables H which satisfy the condition that zero is the left endpoint of the 1-α HPD of the random variable Hy-Hy,I „0. If Em denotes the set of medians of the probability densities of the random variables in E, then the minimal detectable dose of the assay is the supremum ooff EE .. TTyyppiiccaallllyy αα == 00..0055 oorr lleessss,. For computational purposes the supremum is the maximum. Estimation of extra-Poisson variation A quick preliminary calculation of the amount of extra-Poisεon variation iε provided by the approximate z-statistic. For the pair j in group i. If the approximate z-εtatistics exceed an absolute value of four, there is a marked amount of extra-Poisson variation. Computer System Implementation A computer system which implements the method described above is preferably divided into a number of modules which RECTIFIED SHEET (RULE 91 perfor different procedures. A data flow diagram of a suitable computer syεtem and modules within the program, is provided in Fig. 6. In Fig. 6, a model parameter estimator 90 receives asεay data 92, a model 94 for the standard curve, and prior denεities 96 for the standards and controls. The model and the prior densities a typically provided by the user of the εyεtem. The aεεay data may come directly from the detector 40 (Fig. 2) or may have been stored in the memory 60 or 52 of the computer 42 in the interim. In some cases, the assay data may have been stored from prior aεεay runs which used conventional techniques for measuring the concentration of unknown sampleε, and therefore could not, for example, meaεure the accuracy of unknown εampleε assayed in singlet. Using the procedureε outlined above, the model parameter eεti ator 90 determineε an estimate 98 (θ~) of the parameters θ and, if used, σ 2 . Given θ " , the estimated εtandard curve 97 for the aεsay is determined. A z-statiεtic calculator 91 allows a quick preliminary estimate z 93 of extra-Poiεεon variation to be made, uεing the procedureε outlined above. A poεterior estimator 100 receives the estimateε 98, assay data 92 and a prior density 102 for the unknowns. Using the procedures outlined above, the posterior estimator 100 provides a poεterior density 104 for each of the unknown samples, from which estimateε of the concentration such the median m or mean μ and estimateε of the accuracy, εuch aε the standard deviation σ and coefficient of variation σ/μ may be calculated. The poεterior eεtimator 100 alεo determines the posterior density 106 for the apparently blank standards. An MDD calculator 108 receives the posterior densities 106 for the apparently blank standardε and determines, using the procedures outlined above, the minimal detectable dose (MDD) 110 for the asεay run. A similar data flow diagram for a second embodiment of the invention is shown in Fig. 7. In this embodiment, there is no model parameter estimator 90, but rather posterior estimator 112 includes a model parameter posterior estimator 114 which integrateε out the model parameters while calculating the posterior densities using the procedures outlined above. In this process, an estimate 113 of the model parameters θ, and σ 2 if used, is provided from which an estimated standard curve 115 is determined. This embodiment is otherwise similar to the first embodiment. A module dependency diagram, illustrating how different modules of the first embodiment of the computer system described above are interrelated, is illustrated in Fig. 8. In Fig. 8, a controller 120 provides interaction with a user and an assay controller 122, allowing a user to specify which assay resultε are to be analyzed. The aεsay controller 122 is a conventional assay controller which is used to obtain raw response data from an assay run. The controller 120, after receiving assay data enables the model parameter estimator 90 to determine an estimate of the model parameters. When the model parameter estimator has completed its operations, the controller 120 enables the posterior estimator 100 to estimate posterior densities for the unknowns and blank standards. Finally, the controller 120 enables the MDD calculator 110 to determine the MDD of the assay run, and then provides the results to the user via an output of the computer 42. A module dependency diagram, illustrating how different modules of the second embodiment of the computer system described above are interrelated, is illustrated in Fig. 9. In this embodiment, the controller 124 iε slightly different from controller 120 of Fig. 8, in that it controls a different posterior estimator 112 (Fig. 7). The posterior estimator 112 controls the model parameter posterior estimator 114 in order to integrate out over the model R£CT,F,ED SHEET(RULE 91) parameterε while determining the posterior densities of the unknowns and blank standards. The operation of controller 124 is otherwise similar to that of controller 120. Example: Cortisol Radioligand Binding Assay Run Cortisol, a steroid hormone produced by the adrenal glandε, iε responsible for regulating metabolism, the immune system and the streεε reεponεe of the body. The plasma concentration of cortisol is generally reported in units of micrograinε per deciliter (μg/dl) and itε phyεiologic range under nonstresε conditionε extends from 3 to 24 μg/dl. In caseε of Cuεhing's syndrome or high eεtrogen states, the plasma concentrations may be aε high aε 40 to 60 μg/dl. The working range of the cortiεol aεεay extends from 1 to 60 μg/dl. Since the weight of one cortisol molecule iε 6.02 x 10 ~ μg it follows that h„ = -35.05. Because the largest concentration for a standard sample is 60 μg/dl, h, was set to be 4.79. The prior probability density for cortiεol uεing these specifications with v = 6 in equation (10) is shown in Figure 10. The example asεay data consist of six cortisol standards prepared at concentrations of 0, 1, 3, 10, 25, and 60 (μg/dl) and their aεεociated reεponses (measured in the number of radioactive counts); three control cortisol samples at concentrations 3.6, 18.5, and 29.5 (μg/dl) and their aεεociated reεponεeε and the reεponεeε from 10 εampleε containing unknown concentrationε of cortiεol. All εampleε were aεεayed in duplicate. The table of Figure 11 εhowε the data and the amount of extra-Poiεεon variation in each pair. Four of the six standard responεe pairs, two of the three control response pairs and three of ten unknown responεe pairε have approximate z-statistics which exceed four in absolute value suggesting that this assay run has a marked amount of extra-Poisson variation and hence, experimental error. To obtain preliminary parameter eεtimateε, the Rodbard form of the 4-PL model was used. The value of "max" was set to be equal to the largest observed response plus 5% and "min" was set to be equal to the smallest observed response minus 5%. Using all the standard and control pairs except those corresponding to the apparently blank standards, preliminary eεtimateε of ε and p (see equation 3) in the Rodbard formulation were computed by ordinary least squareε, ignoring for the preεent the experimental error in the prepared hormone εampleε. Theεe preliminary eεtimateε were uεed aε εtarting values for fitting the model of the standard curve without experimental error to the standard and control data by maximum likelihood estimation. Theεe maximum likelihood estimates of the parameters were then used along with different starting values for the experimental error variance σ 2 to fit the full model to the standards, controls, and unknowns. The negative of the log-likelihood in equation (9) is minimized, for example by using the IMSL subroutine DBCONF with the integration in equation (6) being computed by an algorithm based on Laplace's method. The Laplace method used in this example iε described in the Appendix, and relies on Inference and Disputed Authorεhip: The Federaliεt, by F. Mosteller et al. , (Reading, Masεachuεettε: Addiεon-Weεley, 1964), p. 154, ( "Moεteller" ) and Aεymptoticε and Special Functionε, by F.W.J. Olver, (New York: Academic Preεε, 1974), pp. 43 and 155, ("Olver"). Observed information matrices and standard errors were computed using the procedures described in "Computing observed information by finite differences," by R.E. Kasε, in Communicationε in Statiεtics-Simulations, Volume 16, 1987, pp. 587-599. Theεe procedureε provide information deεcribing how the asεay data contribute to parameter eεtimates, as will now be described. The model parameter eεtimateε and their εtandard errors are given in the table in Fig. 12. To asεeεε the value of uεing the controlε and the unknowns along with the standards to eεtimate the model parameterε, the table in Fig. 12 shows the diagonal elements of the observed information matrix decomposed into individual additive components from the standards, the controlε and the unknownε. The controls contribute information to the estimation of θ and σ 2, and in fact, the percentages of information obtained from the controlε for estimating γ and σ 2 are greater than those obtained from the standards. These percentages for γ are 86.24% from the controls compared 2 with 11.87% from the standardε and for σ , 43.1% compared with 34.33%. The unknowns contribute 22.57% of the information for the eεti ation of σ 2, and therefore, εhould be included in the analyεiε to determine better the magnitude of thiε parameter. As expected, the unknowns provide negligible information for the estimation of θ. The interpretation of σ for this example aεsay run iε that the experimental error waε leεε than + 20% (= + 1.96 * {exp (σ)-l}) for moεt (95%) of the assay samples. This information cannot be obtained using conventional methods. Figures 13 and 14 show the plots of the posterior densitieε of the εtandards and controlε baεed on the obεerved εingle responseε and on observed response pairs. The εummary εtatiεticε for theεe densities are given in the table of Fig. 15. The plots in Figures 13 and 14 and the results in these tables show that the posterior densitieε baεed on either the εingle or paired reεponses are symmetric with a small right skew in that, for each, the median exceeds the mode and both are less than the mean. The percentage coefficient of variation for the single reεponεe poεterior denεitieε range from 10.1% to 17.9% and from 7.1% to 10.0% for thoεe baεed on responεe pairs . All the individual poεterior medianε approximate reaεonably well the intended cortiεol concentrationε for the εtandards and the controls with the exception of thoεe correεponding to the poεterior density based on the first response, 18,747, for the control with an apparently blank concentration, and the single responεe posterior density for the response of 9,857 corresponding to estimating the second control with an intended cortisol concentration of 10 μg/dl. The respective medians are 0.50 and 8.08. The eεtimates of each based on the paired responεe poεterior denεitieε, 0.35 and 8.76, reεpectively are cloεer to the intended concentrationε. The table in Fig. 16 εhowε the εummary εtatistics for the posterior densitieε for the unknownε and the MDD. The coefficientε of variation for unknowns have the same pattern aε that for the εtandards and controlε. If α is .05 in the calculation of the MDD, then the estimated MDD or senεitivity iε 0.45. The relationεhip between the poεterior denεity whoεe median iε the eεtimated MDD and the posterior density corresponding to the responseε obtained from assaying the apparently blank standards is shown in Figure 17. Discuεεion One advantage of the method of the preεent invention iε that the estimates using Bayes' rule consider explicitly the distribution of the analyte given the responses. By developing a prior density for the analyte concentration based on known properties of the aεεay and modeling εpecifically the experimental error, this method offers several other distinct advantages. First, by modifying the model of the standard curve to characterize explicitly the experimental error on the analyte concentration εcale instead of on the responεe εcale, this εource of error can be separately identified as uncertainty in the analyte in any asεay data analyεiε. Second, the uεe of the unknownε and the controls along with the εtandardε in the eεtimation of θ and σ 2 allows the magnitude of the experimental error to be determined more accurately. Furthermore, because the parameter σ 2 is the mean square relative experimental error, the estimate of the error can be understood more readily in terms of the assay than the exponent on the power function in the relative error model of Rodbard. When samples are assayed with more than one replicate, the approximate z-statistic may be used to identify the posεible magnitude of the aεsay experimental error prior to any model fitting. For an analyte asεayed with at leaεt 3 replicateε, this statistic could take the increase with the number of εampleε whose approximate z-statiεtic exceedε four in abεolute value. Second, the prior denεity selected accurately summarizes the manner in which the laboratory chemist useε prior knowledge of the deεign of the aεεay and allowε two objectives of the asεay experiment to be accompliεhed: 1) the aεεay experiment iε an independent εource of information and 2) the MDD for the assay run may be computed. The foregoing example also demonstrateε that the bulk of the information on the concentration of analyte in a εample and itε accuracy are provided by the data through the eεtimated probability denεity of the reεponεe (equation 6) and not by the prior denεity. That iε, the interval of effective non-zero εupport of the poεterior denεitieε iε markedly smaller than that of the prior density and the ratio of the heights of the posterior densities at the modes to that of the prior density at its mode ranges from 3 to 500 for the posterior densities based on singletε and from 5 to 550 for those based on replicateε. If the analyεiε of a given εample were to suggest that its analyte concentration might exceed the upper limit of the effective non-zero εupport of the prior denεity, then serial dilutions of the sample could be made and the analysiε performed on the diluted sampleε. The appropriate error denεity of the original εample could then be computed from the posterior densities of the diluted sampleε by variable transformations. A third advantage of this method is that, conditional on θ, the posterior probability densities provide a complete description of the accuracy in a determination of an analyte concentration. The median of the poεterior denεity waε εelected aε the eεti ate of the analyte becauεe this measure of location is invariant under transformation from h to H. Aε a fourth advantage, it iε now possible to compute a posterior denεity baεed on the responseε for any number of replicateε for a εtandard, control or unknown εample and therefore, to eεtimate the accuracy in εpecimenε assayed as singlets. In addition, all the assay calibration statiεticε e.g., the coefficientε-of-variation and the MDD, are maximum likelihood estimates characterized with respect to well-defined probability densities. Fifth, the new definition of the MDD allows for a more accurate determination of the concentration of analyte which the asεay can juεt diεtinguiεh from a blank concentration given that all specimens are asεayed with error. That iε, εuppoεe, aε part of a calibration experiment, that a εet of N. dilutionε of the εmalleεt non-blank εtandard are aεεayed in addition to the typical εet of εtandardε and controlε. For j = l, ..., N , let H |H . denote the random variable aεεociated with the poεterior denεity obtained from assaying the j dilution. Because each εpecimen iε aεsayed with error, both the posterior denεitieε of the H 's and thoεe of the random variableε H - H ,_ are uεed. In this experiment the elements of E will come from the H |H 4 -'s since it iε unlikely that specimens whoεe intended concentrations exceed that of the εmaliest noπ-bIan εtandard will satiεfy the MDD definition above. The estimated analyte concentrationε which are juεt εtochaεtically different from an apparent blank belong to the set E and if the number of replicates asεayed for each specimen is one, then E has a εingle element which is the MDD. If the number of replicateε iε greater than one, then Em haε more than one element and the sup c remum of Em is choεen aε the MDD. Sixth, under this approach consideration of the accuracy of the blank sample concentration and any other sample concentration is made explicit by the H - H v ι 0 's, therefore obviating the need to have three separate definitions of the MDD such as given by Oppenheimer. Since the MDD may be computed for any asεay run which includeε εtandardε having apparently blank concentrationε, any concentration detected between the MDD and the lowest non-blank standard can be reliably reported aε part of the usual response analysiε. Including dilutionε of the lowest non-blank standard provideε better definition of the behavior of the aεεay at lower concentrationε and may increase the estimation precision of θ and σ 2. How many dilutionε to include iε dependent on the experimental deεign. Having now described a few embodiments of the invention, it should be apparent to those skilled in the art that the foregoing is merely illustrative and not limiting, having been presented by way of example only. Numerouε modificationε and other embodimentε are within the εcope of one of ordinary skill in the art and are contemplated aε falling within the εcope of the invention aε defined by the appended claimε and equivalentε thereto. ΛPPENDIX We derive the Laplace approximation to the Gaussian mixture of Poisson densities defined by the integral in (6) From Olver (1974, p.155) the Laplace approximation to the integral I = £* e - D P< v) q(v)dv is I~^ q( )e- n ^{l + ^ + 0(n- 2 )}, (np 2 )= <• 2n J where f , = ( 12q2 _ 3121 _ £ i + *Ϊ!*I) / (12p2)> P2 P2 P 2 / and pi , q j are respectively the i lh derivatives of p and q evaluated at v, where v is the point at which p assumes its minimum on the interval (b>ι,b 2 ). For evaluating ) we follow Mosteller and Wallace (1964, p.154) and set n = 1. The logarithm of the integrand in (3.2) has components •logr bi j .Vi jf c.ø] = Ylogg-g-logY!, = -v 2 /(2 σ 2 )-(log2τr)/2-log<7, where Y = Yjjk, v = v; jk and g(h,#) is given by (2.1). Hence, using Stirling's ap¬ proximation to the factorial function JogY! ~ (log2π)/2 -J- (Y- l/2)logY-Y, setting P( v ) = δ( v )- γio δ{g( v )}+ v2 /( 2σ2 ) z^d q(v)=l, the Laplace approximation to (O) S where g = g(v). The IMSL numerical minimization routine UVMID is to <i.-H<τ:n:ι ' v. The importance of evaluating the term f 2 /2 is both to establish that the asvmpioiics have taken effect, and to increase the accuracy of the approximation. Λ small value for the second term, f 2 /2, compared to the first term, 1.0, indicates that the Laplace approximation is likely to be accurate (Olver, 1974, p.43). In the integrations performed as part of the data analysis in Section 5 this term was consistently on the order of 10 ~5 . To evaluate f 2 and p 2 we require the derivatives of p and g with respect to v. These are σ σv 3 \ g) g 2 dv σv 2 g 3 ovJ J For the derivatives of* g with respect to v, let e = exp(β — j( + v)), K = (max - min)e/(e-M) and r = 7/(e+l), then we have dg = ><'(c-l), f = Kr'(l - 4c + <_'), f^f = Kr < (e-l)(l-]0<_ + e ! ) 7V 4

Claims: Claims 1. A method for measuring a concentration of an analyte in a sample having an unknown concentration of the analyte using an affinity assay having an experimental indicator wherein an assayed εample emits, in a predetermined period of time, an amount of the experimental indicator, which amount is a response emitted by the sample, comprising the steps of: assaying standard samples, each having a known concentration of the analyte; detecting a response emitted by each standard sample in the predetermined period of time; assaying control samples, each having a known concentration of the analyte; detecting a response emitted by each control sample in the predetermined period of time; assaying the unknown sample; detecting a response emitted by the unknown sample in the predetermined period of time; supplying a prior probability density for the unknown concentration; supplying a probability model which specifies a standard curve relating an expected response to a concentration of the analyte; generating a posterior density, having a dispersion, a coefficient of variation, a variance, and a resolution and a mean, a mode and a median, for the unknown concentration based on the supplied prior density, the supplied probability model, and the responses for the standard sampleε, the control samples and the unknown samples, by applying Bayes' rule; and providing one of the mean, mode and median of the posterior density as a measure of concentration of analyte in the unknown sample. 2. The method of claim 1, further comprising the step of providing a summary of the disperεion of the poεterior denεity aε a meaεure of accuracy of the provided meaεure of concentration. 3. The method of claim 1, further compriεing the εtep of providing a meaεure of accuracy of the measure of concentration of the analyte, wherein the measure of accuracy is defined in terms of the poεterior denεity. 4. The method of claim 3, wherein the meaεure of accuracy is defined by the coefficient of variation of the poεterior density. 5. The method of claim 3, wherein the measure of accuracy is defined by the resolution of the posterior density. 6. The method of claim 3, wherein the measure of accuracy is defined by the variance of the posterior density. 7. The method of claim 1, further comprising the step of determining a minimal detectable dose, thereby defining a lowest analyte concentration which can be detected. 8. The method of claim 3, further comprising the step of decomposing the measure of accuracy into an experimental error and a reεponεe error. 9. The method of claim l, wherein the poεterior denεity iε determined using estimated parameters of the supplied probability model. 10. The method of claim 9, wherein the estimated parameterε are determined using a weighted least-sguareε technique and using the responses obtained for the standard samples. 11. The method of claim 9, wherein the estimated parameters are determined using maximum likelihood estimation and using the responses obtained for the standard sampleε. 12. The method of claim 9, wherein the eεtimated parameterε of the probability model are derived uεing the reεponses for the standard εampleε and the unknown samples. 13. The method of claim 9, wherein the estimated parameters are derived uεing the responseε for the εtandard εampleε and for the control εamples. 14. The method of claim 9, further comprising the step of integrating out the estimated parameters of the probability model to account for uncertainty in these parameterε while deriving an inference for the measure of the analyte concentration of the unknown sample. 15. The method of claim 1, wherein the probability model haε a εtandard curve which switch is derived from a maεε-action law for an underlying chemical reaction of the affinity aεsay. 16. The method of claim 1, wherein the probability model used to specify the standard curve includes a random variable which repreεentε variationε in concentration of analyte due to variability in preparation of sampleε, whereby the posterior density is a mixture of random variables. 17. The method of claim 7, further comprising the εtep of determining a posterior density for apparently blank standard sampleε, and a higheεt probability density interval having a left endpoint and a right endpoint, such that the minimum detectable dose is a maximum value of a set of medianε of probability denεitieε of random variables H for which zero is the left endpoint of the higheεt probability denεity interval of random variables H -H . „ . 18. A system for measuring a concentration of an analyte in a sample having an unknown concentration of the analyte uεing an affinity aεεay having an experimental indicator wherein an aεεayed εample emitε, in a predetermined period of time, an amount of the experimental indicator, which amount iε a reεponεe emitted by the εample, compriεing: a detector arranged to detect the response of a sample, and providing an output indicative of the responεe detected; a computer system having an input connected to receive the output from the detector, and including a memory and a procesεing unit, the memory having stored therein a prior probability denεity for the unknown concentration and a probability model which εpecifies a standard curve relating in expected response to a concentration of the analyte, the procesεing unit including: meanε for controlling the aεεay and the detector to detect a reεponεe of a plurality of εtandard εampleε, a plurality of control εampleε and the unknown εample in the predetermined period of time; meanε for generating a poεterior denεity having a dispersion, a coefficient of variation, a variance, a resolution, a mean, median and mode, for the unknown concentration based on the stored prior denεity, the stored probability model, and the responses for the standard samples, the control εampleε, and the unknown samples, by applying Bayes' rule; and meanε for communicating one of the mean, median and mode of the posterior density aε a measure of the concentration of analyte in the unknown sample. 19. The syεtem of claim 18, further compriεing meanε for communicating a summary of the dispersion of the posterior denεity aε a meaεure of accuracy of the meaεure of the concentration. 20. The εyεtem of claim 18, further compriεing meanε for communicating a measure of accuracy of the measure of concentration of the analyte, wherein the measure of accuracy is defined in terms of the poεterior density. 21. The system of claim 20, wherein the measure of accuracy iε defined by the coefficient of variation of the poεterior density. 22. The system of claim 20, wherein the measure of accuracy is defined by the resolution of the posterior density. 23. The syεtem of claim 20, wherein the measure of accuracy is defined by the variance of the posterior density. 24. The εyεtem of claim 18, further comprising means for determining a minimal detectable dose, thereby defining a lowest analyte concentration which can be detected. 25. The syεtem of claim 20, further compriεing means for decomposing the measure of accuracy into an experimental error and a reεponεe erro . 26. The εyεtem of claim 18, wherein the posterior density is determined using estimated parameters for the supplied probability model. 27. The system of claim 26, wherein the estimated parameters are determined using a weighted least-εguares technique and using the responses obtained for the εtandard samples. 28. The syεtem of claim 26, wherein the eεtimated parameterε are determined using maximum likelihood estimation and using the responses obtained for the standard εampleε. 29. The εyεtem of claim 26, wherein the eεtimated parameterε are derived uεing the reεponεes for the standard samples and the unknown sampleε. 30. The syεtem of claim 26, wherein the eεtimated parameterε are derived using the responεeε for the εtandard εampleε and for the control εamples. 31. The syεtem of claim 26, further comprising means for integrating out the estimated parameterε of the probability model to account for uncertainty in theεe parameterε while deriving an inference for the meaεure of the analyte concentration of the unknown sample. 32. The syεtem of claim 18, wherein the probability model εpecifieε a εtandard curve which iε derived from a maεε-action law for an underlying chemical reaction of the affinity aεεay. 33. The εyεtem of claim 18, wherein the probability model includes a random variable which represents variations in concentration of analyte due to variability in preparation of sampleε, whereby the poεterior density is a mixture of random variableε. 34. The system of claim 18, further comprising meanε for determining a posterior density for apparently blank standard samples, and a highest probability density interval having a left endpoint and a right endpoint, such that the minimum detectable dose is a maximum value of a set of medians of probability densitieε of random variableε H for which zero iε the left endpoint of the highest probability density intervals of the random variables H -H . _ . 35. A method for measuring a concentration of an analyte in a sample having an unknown concentration of the analyte using an affinity aεεay having an experimental indicator, wherein an aεεayed εample emitε in a predetermined period of time an amount of the experimental indicator, which amount iε a reεponεe emitted by the εample, the method compriεing the εtepε of: aεsaying a plurality of known sampleε, each having a known concentration of the analyte; detecting a reεponεe emitted by each known sample in the predetermined period of time; asεaying the unknown εample; detecting a reεponse emitted by the unknown sample in the predetermined period of time; supplying a prior probability density for the unknown concentration and a probability model which εpecifieε a εtandard curve relating an expected reεponεe to a concentration of the analyte; generating a poεterior denεity for the unknown concentration baεed on the εupplied prior denεity, the εupply probability model, and the reεponεeε for the known sampleε and the unknown εampleε, by applying Bayeε ' rule; and providing a characteriεtic of the poεterior denεity as a measure of concentration of analyte in the unknown sample.

Inventors: BROWN EMERY N, SKATES STEVEN J, BROWN, EMERY, N., SKATES, STEVEN, J.
Applicants: GEN HOSPITAL CORP, THE GENERAL HOSPITAL CORPORATION
Application year: 1994
Main classification: G01N33
IPC codes: G01N33, G01N35